To help better understand the inherent biological signals in the recently published single-cell RNA-seq data from BALF cells [1]} for COVID-19 severity prediction, we develop a bioinformatics pipeline to first identify the COVID-19 severity biomarker genes and then classify BALF cells using these genes. We apply several classification algorithms, including linear classifiers, such as Linear Discriminant Analysis (LDA), and non-linear ones, such as Quadratic and Flexible Discriminant Analysis (QDA and FDA [2]}), Random Forests (RF) [3]} and Support Vector Machines (SVM), to evaluate our pipeline in classifying BALF cells based on COVID-19 severity.
